https://github.com/AlfredoZermini/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Papers with Code 아카이브
Skeleton Based Action Recognition
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Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Anak2016/GAT
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Anou9531/GAT
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/BIG-S2/keras-gnm
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/BehnooshParsa/HumanActionRecognition_with_ErgonomicRisk
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/BlurryLight/DD-Net-Pytorch
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ColdenChan/GAT_D
https://github.com/Diego999/pyGAT
https://github.com/Dir-b/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/DixinFan/st-gcn
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/DylanWusee/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ECE685-FinalProject/3D-object-recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ELKYang/2s-AGCN-paddle
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ElliotHYLee/VisualOdometry3D
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/ElricXin/Skeleton-MixFormer
https://github.com/Fragjacker/Pointcloud-grad-CAM
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/FrederikSchorr/sign-language
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/GOD-GOD-Autonomous-Vehicle/self-pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Gbouna/Action-Recognition-for-Privacy-Preserving-Ambient-Assisted-Living
https://github.com/Gbouna/HT-ConvNet
https://github.com/GeyuanZhang/st-gcn-master
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/GraphSAINT/GraphSAINT
https://github.com/HamzaGbada/GCN-Numpy
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.